Context Hierarchy Research Articles

Conscientiousness and perceived ethicality: Examining why hierarchy of authority diminishes this positive relationship

AbstractHuman resource (HR) managers hire conscientious employees because they are both productive and are viewed as upholding high ethical standards due to their propensity to engage in voice. Organizations may strive to create a work context conducive to all employees acting ethically, not just conscientious ones, by centralizing decision‐making authority and promoting formalization through a higher hierarchy of authority. Yet, we propose that from the social information processing perspective, in higher hierarchy of authority contexts, peers may view their highly conscientious colleagues as less ethical. We hypothesize these effects through the lens of trait activation theory, according to which in a higher hierarchy of authority context, others are less likely to notice the voice behaviors of conscientious employees. Problematically, when others fail to notice conscientious employees' voice, they may perceive these workers as being less ethical. We tested our hypothesized moderated mediation model in a matched sample of employees (N = 820), their supervisors (N = 445), and peers (N = 529). As predicted, hierarchy of authority moderated the positive relationship between conscientiousness and voice, which in turn explained others' perceptions of their ethicality. Conscientiousness was positively related to peer assessments of ethicality via promotive (not prohibitive) voice when hierarchy of authority was lower (but not higher), partially supporting our hypotheses. These results suggest HR practitioners should be cognizant of the differential evaluations of highly conscientious employees in contexts with different levels of hierarchy of authority, and continuing challenges associated with balancing flexibility and formalization.

The Utilization of Functional Equivalence Theory and Context Hierarchy Theory in the Translation of Excellent Traditional Chinese Culture

The Utilization of Functional Equivalence Theory and Context Hierarchy Theory in the Translation of Excellent Traditional Chinese Culture

Effects of Verb Network Strengthening Treatment (VNeST) using Telepractice on Word Retrieval in Korean-Speakers with Non-Fluent Aphasia

Objectives: Verb Network Strengthening Treatment (VNeST) is a semantic treatment that aims to improve lexical retrieval across a hierarchy of linguistic contexts, including sentences and discourse. So, the purpose of this study was to investigate the effects of VNeST using telepractice on Korean individuals with moderate to severe non-fluent aphasia and examine the advantages of telepractice treatment. Methods: Three Korean individuals with aphasia caused by a single lesion in the left hemisphere participated in this study. They received VNeST via ZOOM in real time at their home. VNeST protocol consisted of 6 steps: step 1, generate three agents and themes for the target verb; step 2, read each scenario aloud; step 3, choose the appropriate case marker and answer three wh-questions about agent-theme pairs; step 4, judge semantic plausibility of sentences; step 5, confront naming on animation target verb task; step 6, repeat the prompts of Step 1 without cues. Multiple-baseline design across behaviors was employed, and effect sizes of the treatment were obtained. Results: The results of this study are as follows: 1) all participants showes large effect sizes, 2) all participants showed a generalization effect, 3) the CIU rate of all participants improved, 4) all participants showed increased score or performance on language assessment task, 5) all participants and their careers responded that they were very satisfied with the telepractice treatment. Conclusion: The current study suggest that VNeST via telepractice is effective as off-line treatment in promoting generalization from single word naming in person with moderate to severe non-fluent aphasia.

Iterated team semantics for a hierarchy of informational types

In this paper, we introduce and study a framework that is inspired by the team semantics for propositional dependence logic but deviates from it in several respects. Most importantly, instead of the two semantic layers used in dependence logic – possible worlds and teams – a whole hierarchy of contexts is introduced and different types of formulas are evaluated at different levels of this hierarchy. This leads to a rich stratification of informational types. In this framework, the dependence operator of dependence logic can be defined by the standard propositional connectives (negation, conjunction, disjunction and implication). We explore the formal aspects of this approach and apply it to a number of puzzling phenomena related to modalities and conditionals.

The ‘context’ of transport project cost performance: Insights from contract award to final construction costs

Despite the plethora of studies examining the cost performance of transport projects, we still do not fully understand why they exceed their agreed price for construction. A lack of an in-depth exploration of context has contributed to this lack of understanding. In this paper, we seek to provide a context as to why the construction costs of transport projects experience increases from their contract award. We adopt sense-making approach, which is qualitative in nature, to examine the performance and financial reviews for eight transport projects constructed by an Australian contractor. The reviews are checkpoints undertaken during the construction of projects to monitor actual costs and forecasted profits for the contractor. The reviews are performed at the 50% and 75% milestones of a project's forecasted schedule by a team independent from the contractor's organization. We look into context states of projects such as their programme, quality, safety, design, and management. We use a context breakdown structure to uncover the ‘contexts within contexts’ that significantly contribute increases to construction costs. We reveal that the mean forecasted contractor margin was almost 9%, which reinforces the belief that there is a lack of competition in the marketplace. Overall, the hierarchy of contexts within contexts we unravel provides further understanding as to why transport projects experience increases in their construction costs. Considering the nature of the recurring contexts that we identify, we recommend that governments re-calibrate their approaches to procuring their transport projects. We suggest that they embrace negotiated contracts, alliance contracting, leadership and resourcing strategy, and work toward establishing a generative culture in the projects they procure.

Contextual ontology for tonality assessment

Abstract In the paper we discuss the possibilities of using hierarchical contextual ontologies for supporting sentiment classification tasks. The discussion focuses on two important research hypotheses: (1) whether it is possible to construct such an ontology from a corpus of textual document, and (2) whether it is possible and beneficial to use inferencing from this ontology to support the process of sentiment classification. To support the first hypothesis we present a method of extraction of hierarchy of contexts from a set of textual documents and encoding this hierarchy into a multi-level contextual ontology. To support the second hypothesis, we present a method of reasoning from the ontology, and results of experimental verification, which show that use of this reasoning method can increase the accuracy of sentiment classification for longer text documents.

A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand

Forest fires are abrupt transformations of the natural ecosystem and management authorities are required to take preventive measures to tackle fire events. Geographic information system (GIS) is a powerful tool for providing information with a spatial context and analytical hierarchy process (AHP) is a well-established technique for multiple criteria decision making. In this study, GIS and AHP are combined to analyse seven fire-related factors related to climate, topography and human influence. Fire risk for a peat swamp forested area in Kuan Kreng, Nakorn Sri Thammarat province, Thailand is estimated in five categories. 705 historic fire events from 2006 to 2017 are used to validate our approach. 82% of the historic fire incidents occurred within the highest fire risk class categories while only a few omission errors were recorded. The combined approach of GIS and AHP techniques can yield useful fire risk maps, which can consequently be used for future planning and management of fire prone areas.

Hierarchical Contextual Refinement Networks for Human Pose Estimation.

Predicting human pose in the wild is a challenging problem due to high flexibility of joints and possible occlusion. Existing approaches generally tackle the difficulties either by holistic prediction or multi-stage processing, which suffer from poor performance for locating challenging joints or high computational cost. In this paper, we propose a new Hierarchical Contextual Refinement Network (HCRN) to robustly predict human poses in an efficient manner, where human body joints of different complexities are processed at different layers in a context hierarchy. Different from existing approaches, our proposed model predicts positions of joints from easy to difficult in a single stage through effectively exploiting informative contexts provided in the previous layer. Such approach offers two appealing advantages over state-of-the-arts: (1) more accurate than predicting all the joints together and (2) more efficient than multi-stage processing methods. We design a Contextual Refinement Unit (CRU) to implement the proposed model, which enables auto-diffusion of joint detection results to effectively transfer informative context from easy joints to difficult ones. In this way, difficult joints can be reliably detected even in presence of occlusion or severe distracting factors. Multiple CRUs are organized into a tree-structured hierarchy which is end-to-end trainable and does not require processing joints for multiple iterations. Comprehensive experiments evaluate the efficacy and efficiency of the proposed HCRN model to improve well-established baselines and achieve new state-of-the-art on multiple human pose estimation benchmarks.

Multiscale Deep Alternative Neural Network for Large-Scale Video Classification

With the rapid increase in the amount of multimedia data, video classification has become a demanding and challenging research topic. Compared with image classification, video classification requires mapping a video that contains hundreds of frames to semantic tags, which poses many challenges to the direct use of advanced models originally designed for image-oriented tasks. On the other hand, continuous frames in a video also give us more visual clues that we can leverage to achieve better classification. One of the most important clues is the context in the spatiotemporal domain. In this paper, we introduce the multiscale deep alternative neural network (DANN), a novel architecture combining the strengths of both convolutional neural network and recurrent neural networks to achieve a deep network that can collect rich context hierarchies for video classification. In particular, the DANN is stacked with alternative layers, each of which consists of a volumetric convolutional layer followed by a recurrent layer. The former acts as a local feature learner, whereas the latter is used to collect contexts. Compared with popular deep feed-forward neural networks, the DANN learns local features and their contexts from the very beginning. This setting enables preserving context evolutions, which we show to be essential for improving the accuracy of video classification. To release the full potential of the DANN, we develop a deeper version with stochastic-layer skip-connections and construct a multiscale DANN to incorporate contexts at different scales. We show how to apply the multiscale DANN for video classification with carefully designed configurations in terms of both input–output settings and training–testing methods. The DANN is shown to be robust to not only human-centric videos, but also natural videos. As there are few large-scale natural disaster video datasets, we construct a new large-scale one and make it publicly available. Experiments on four datasets show the effectiveness of our method for both human actions and natural events.

‘Becoming others’

This paper reviews a qualitative study of student and lecturer communication and engagement within a ‘safe’, ‘structured’ setting. Relationships between students and academics are part of an emotional as well as intellectual encounter. Our sense of self is profoundly affected by how we feel others think about us. Being heard and valued gives us the confidence to question. Yet within the university context hierarchies and positions easily inhibit this rich potential. The prevalent focus on performativity and individual success can undermine our shared encounters, isolating us so that group work, endemic within university processes becomes, tokenistic and superficial. The study embedded ‘selves’ within a small group setting that facilitated a sense of belonging, open mindedness and compassion towards others by sharing thoughts and experiences. Students and academics exchanged roles and responsibilities. The results suggest that if this type of group work was extended and implemented into the wider university practice, certain concerns about inclusion and equity could be addressed and students and lecturers could enjoy a deeper intellectual and emotional affinity.

Duality in non-abelian algebra II. From Isbell bicategories to Grandis exact categories

This paper continues the study of self-dual axioms on forms, i.e. faithful amnestic functors, motivated by properties of subobject forms in non-abelian algebra (which in many cases are Grothendieck bifibrations), and in particular, by properties of forms of substructures of group-like structures. In this paper we explore axiomatic origins of this kind, of a hierarchy of contexts introduced by M. Grandis for his projective approach to non-abelian homological algebra. This reveals new links between those contexts and the theory of factorization systems. Among other things, we show that a Grandis exact category is the same as an Isbell bicategory whose form (fibration) of projections is isomorphic to the form (opfibration) of injections.

A Representation of Hierarchical Spatial Context for Urban Computing

Context of contents for optimal user experience can be composed of various elements including time, spatial context, and social context. And also including global, local and personal contexts. In this paper, we proposed a representation scheme of spatial context hierarchy for optimal content services. Proposed context representation consists of time and spatial index and relations makes formal representation. Spatial Context is presented by spatial elements for spatial information includes point, line and region with elements attribute values

A Cross-Lingual Pattern Retrieval Framework

We introduce a method for learning to grammatically categorize and organize the contexts of a given query. In our approach, grammatical descriptions, from general word groups to specific lexical phrases, are imposed on the query's contexts aimed at accelerating lexicographers' and language learners' navigation through and GRASP upon the word usages. The method involves lemmatizing, part-of-speech tagging and shallowly parsing a general corpus and constructing its inverted files for monolingual queries, and word-aligning parallel texts and extracting and pruning translation equivalents for cross-lingual ones. At run-time, grammar-like patterns are generated, organized to form a thesaurus index structure on query words' contexts, and presented to users along with their instantiations. Experimental results show that the extracted predominant patterns resemble phrases in grammar books and that the abstract-to-concrete context hierarchy of querying words effectively assists the process of language learning, especially in sentence translation or composition.

Cognitively Inspired Decision Making for Software Agents: Integrated Mechanisms for Action Selection, Expectation, Automatization and Non‐Routine Problem Solving

Despite impressive advances in the past decades, autonomous agents living in dynamic and unpredictable environments are typically equipped with simple decision-making mechanisms in their sense-decide-act routines. These agents deal mostly with one goal at a time. This research aspires to model, design and/or implement a sophisticated decision making mechanism that selects the agent's next action with different levels of awareness: automatized skills, consciously mediated routine solutions, and consciously deliberated non-routine solutions. Such a decision-making mechanism is presented in a conscious software agent framework called IDA that implements Baars' Global Workspace Theory of consciousness. IDA integrates many computational and conceptual mechanisms, among which this research deals with its action selection, expectation, automatization and non-routine problem solving modules. The overarching continual task of an agent's intelligence is for the service of choosing, at each moment in time, the appropriate action in response to exogenous and endogenous stimuli. IDA's action selection mechanism (ASM) can interleave and prioritize actions of different and competing goal hierarchies. The ASM system is implemented as a domain independent and reusable framework for behavior networks and is tested as a controller to a khepera robot operating in a real world domain. We humans have the amazing ability to learn a procedural task (e.g. walking) so well that we do not need to think about the task consciously in order to accomplish it. This ability is what we call automatization. Once a task has been automatized there is no need for attention to be paid to its execution unless the expected result does not occur. At failure of expectation, deautomatization process temporarily disables the automatization effects and conscious control plays a role to deal with the failure situation. We implement the automatization and deautomatization cognitive functions as a self-organizing system in the IDA framework. Non-routine problem solving is the ability to devise unexpected, and often clever, solutions to problems that never been encountered before. We will present a detailed design and specification of a non-routine problem solving mechanism as a special goal context hierarchy that guides a deliberative solution search process, which we will discuss in IDA's cognitive cycle.

Contexts and control operations used in accessing list-specific, generalized, and semantic memories

The human ability to focus memory retrieval operations on a particular list, episode or memory structure has not been fully appreciated or documented. In Experiment 1–3, we make it increasingly difficult for participants to switch between a less recent list (multiple study opportunities), and a more recent list (single study opportunity). Task performance was good, although there was a cost associated with switching. In Experiment 4, list-specific learning experiences were used to create a generalized memory as a step towards semantic memory. List-specific memories intruded during attempts to retrieve the generalized memory and the generalized memory enhanced list-specific performance. The generalized memory also intruded in a free-association task. We propose that a hierarchy of contexts and control operations underlie the human ability to access different memory structures and that there is no sharp discontinuity in the control operations needed to access list-specific, generalized, and semantic memories.

Hierarchical noise in large systems of independent agents

A generalization of the coherent-noise models [M. E. J. Newman and K. Sneppen, Phys. Rev. E{\bf54}, 6226 (1996)] is presented where the agents in the model are subjected to a multitude of stresses, generated in a hierarchy of different contexts. The hierarchy is realized as a Cayley-tree. Two different ways of stress propagation in the tree are considered. In both cases, coherence arises in large subsystems of the tree. Clear similarities between the behavior of the tree model and of the coherent-noise model can be observed. For one of the two methods of stress propagation, the behavior of the tree model can be approximated very well by an ensemble of coherent-noise models, where the sizes $k$ of the systems in the ensemble scale as $k^{-2}$. The results are found to be independent of the tree's structure for a large class of reasonable choices. Additionally, it is found that power-law distributed lifetimes of agents arise even under the complete absence of correlations between the stresses the agents feel.

COMBINED NEURAL-NET/KNOWLEDGE-BASED ADAPTIVE SYSTEMS FOR LARGE SCALE DYNAMIC CONTROL

The control of small-scale systems using either knowledge-based or neural net methods is quite feasible. Large scale systems, however, introduce complexities in modeling and excessive computation time. This paper attacks these difficulties by breaking down the problem into a hierarchy of control contexts. The lowest level of this hierarchy is implemented as rule sets and/or neural networks. A method using "hints" is shown to greatly reduce training time in back-propagation neural nets.

Attachment to the residential neighbourhood: a framework for analysis

There is an abundance of literature on attachment to the residential neighbourhood and satisfaction with it. Efforts to explain variation in such attachment emphasise the relevance of population characteristics to the neglect of factors affecting attachment which are external to the neighbourhood's population. This article suggests a wide framework for analysis. The suggestion is made that a hierarchy of neighbourhood contexts could be used in order to reach a more complete explanation and understanding of attachment and variation in it to the residential neighbourhood.

Faster retrieval from context trees

Context trees provide a convenient way of storing data which is to be viewed as a hierarchy of contexts. This note presents an algorithm which improves on previous context tree retrieval algorithms. It is based on the observation that in typical uses context changes are infrequent relative to retrievals, so that data can be cached to speed up retrieval. A retrieval is started from the position of the previous retrieval and auxiliary structures are built up to make the search rapid. Algorithms for addition and deletion of data and for garbage collection are outlined.